The future of artificial intelligence (AI) acceleration demands a paradigm shift beyond the limitations of purely electronic or photonic architectures. Photonic analog computing delivers unmatched speed and parallelism but struggles with data movement, robustness, and precision. Electronic processing-in-memory (PIM) enables energy-efficient computing by co-locating storage and computation but suffers from endurance and reconfiguration constraints, limiting it to static weight mapping. Neither approach alone achieves the balance needed for adaptive, efficient AI. To break this impasse, we study a hybrid electronic-photonic-PIM computing architecture and introduce H3PIMAP, a heterogeneity-aware mapping framework that seamlessly orchestrates workloads across electronic and optical tiers. By optimizing workload partitioning through a two-stage multi-objective exploration method, H3PIMAP harnesses light speed for high-throughput operations and PIM efficiency for memory-bound tasks. System-level evaluations on language and vision models show H3PIMAP achieves a 2.74x energy efficiency improvement and a 3.47x latency reduction compared to homogeneous architectures and naive mapping strategies. This proposed framework lays the foundation for hybrid AI accelerators, bridging the gap between electronic and photonic computation for next-generation efficiency and scalability.

翻译：人工智能（AI）加速的未来需要超越纯电子或纯光子架构局限性的范式转变。光子模拟计算提供了无与伦比的速度和并行性，但在数据移动、鲁棒性和精度方面面临挑战。电子存内计算（PIM）通过将存储与计算共置实现了高能效计算，但受限于耐久性和重配置约束，通常只能进行静态权重映射。单一方法均无法实现自适应、高效AI所需的平衡。为打破这一僵局，我们研究了一种混合电子-光子PIM计算架构，并提出了H3PIMAP——一个异构感知的映射框架，能够在电子和光学层级之间无缝编排工作负载。通过采用两阶段多目标探索方法优化工作负载划分，H3PIMAP利用光速实现高吞吐量操作，并利用PIM能效处理内存受限任务。在语言和视觉模型上的系统级评估表明，与同构架构及简单映射策略相比，H3PIMAP实现了2.74倍的能效提升和3.47倍的延迟降低。该框架为混合AI加速器奠定了基础，弥合了电子与光子计算之间的鸿沟，为下一代高效可扩展计算铺平了道路。